Table 5.

Performance comparison of the PIP-EL with other methods on independent dataset.

Method	Matthews’ correlation coefficient (MCC)	Accuracy	Sensitivity	Specificity	AUC	P-value
PIP-EL	0.454	0.748	0.725	0.772	0.820	–
Extremely randomized trees (ERT)	0.433	0.737	0.713	0.762	0.809	0.716
Support vector machine (SVM)	0.332	0.683	0.647	0.720	0.732	0.006
ProInflam	0.100	0.537	0.922	0.152	0.671	0.000007

The first column represents the method employed in this study. The column 2–6 respectively represent the MCC, accuracy, sensitivity, specificity, and AUC value. The last column represents a pairwise comparison of AUC between PIP-EL and the other methods using a two-tailed t-test. P ≤ 0.05 indicates a statistically meaningful difference between PIP-EL and the selected composition (shown in bold).